feat(quant): M7.3 研究行情口径显式化(默认不复权 none,可切 qfq)
- DailyBarRepository.get_range_many / stream_range_many_columns 增加 adjust 参数 (默认 'none')→ SQL 层过滤口径,消除 stock_daily 混 source/adjust 污染因子的风险 - ResearchSpec / SelectionQuery 增加 price_adjustment(none|qfq),随 config_snapshot 落库可溯源;ResearchService._load_daily 与 SelectionService 装配按口径取数 - tests/test_price_adjustment.py:repo 读取按 adjust 过滤(none/qfq 各自命中)、 spec 默认与字段记录;全量 pytest 通过
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@@ -62,6 +62,7 @@ class SelectionService:
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as_of - timedelta(days=query.warmup_days),
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as_of,
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columns,
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adjust=query.price_adjustment,
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)
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financial: dict[str, FinancialIndicator] = {}
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if query.method == "condition" and self._uses_fundamental(query):
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@@ -62,6 +62,10 @@ class ResearchSpec(BaseModel):
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type: str = Field(default="backtest", pattern="^(factor_test|backtest)$")
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universe: UniverseSpec = UniverseSpec()
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price_adjustment: str = Field(
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default="none", pattern="^(none|qfq)$",
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description="研究行情口径:none 不复权(默认)/ qfq 前复权(result 与 config_snapshot 中显式)",
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)
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factors: list[FactorSpec] = Field(min_length=1)
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selection: SelectionSpec = SelectionSpec()
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rebalance: str = Field(default="monthly", pattern="^(weekly|monthly)$")
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@@ -25,6 +25,10 @@ class SelectionQuery(BaseModel):
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"""一次选股查询(v2 §14.2 Selection 输入)。"""
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universe: UniverseSpec = UniverseSpec()
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price_adjustment: str = Field(
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default="none", pattern="^(none|qfq)$",
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description="行情口径:none 不复权(默认)/ qfq 前复权(结果 config_snapshot 中显式)",
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)
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# 研究时点:None → 引擎用 <= 今天最近可用交易日;显式给历史日期即做历史选股
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as_of: date | None = Field(
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default=None, description="选股时点;历史回测/解释用具体日期,当前选股可留空"
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@@ -42,8 +42,14 @@ class DailyBarRepository(Protocol):
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def get_range(self, symbol: str, start: date, end: date) -> list[DailyBar]: ...
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def get_range_many(self, symbols: Sequence[str], start: date, end: date) -> list[DailyBar]:
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"""批量区间查询(研究服务装配面板用,避免逐只查询)。"""
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def get_range_many(
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self,
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symbols: Sequence[str],
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start: date,
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end: date,
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adjust: str = "none",
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) -> list[DailyBar]:
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"""批量区间查询(研究装配面板用);adjust 指定行情口径(none 不复权 / qfq)。"""
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def stream_range_many_columns(
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self,
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@@ -51,6 +57,7 @@ class DailyBarRepository(Protocol):
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start: date,
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end: date,
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columns: Sequence[str],
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adjust: str = "none",
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) -> Iterator[tuple]:
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"""流式(分批 yield)返回 symbol, trade_date(iso str), 数值列(float) 元组。
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@@ -158,16 +158,24 @@ class SqlAlchemyDailyBarRepository:
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).all()
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return [DailyBar.model_validate(r, from_attributes=True) for r in rows]
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def get_range_many(self, symbols: Sequence[str], start: date, end: date) -> list[DailyBar]:
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rows = self._session.scalars(
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def get_range_many(
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self,
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symbols: Sequence[str],
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start: date,
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end: date,
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adjust: str = "none",
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) -> list[DailyBar]:
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stmt = (
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select(StockDailyModel)
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.where(
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StockDailyModel.symbol.in_(list(symbols)),
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StockDailyModel.trade_date >= start,
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StockDailyModel.trade_date <= end,
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StockDailyModel.adjust == adjust, # 研究主口径:不复权(v2 §8)
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)
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.order_by(StockDailyModel.trade_date)
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).all()
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)
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rows = self._session.scalars(stmt).all()
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return [DailyBar.model_validate(r, from_attributes=True) for r in rows]
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def stream_range_many_columns(
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@@ -176,6 +184,7 @@ class SqlAlchemyDailyBarRepository:
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start: date,
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end: date,
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columns: Sequence[str],
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adjust: str = "none",
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) -> Iterator[tuple]:
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"""流式返回 (symbol, trade_date_iso, *float_cols) 元组,分批拉取。
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@@ -193,6 +202,7 @@ class SqlAlchemyDailyBarRepository:
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StockDailyModel.symbol.in_(list(symbols)),
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StockDailyModel.trade_date >= start,
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StockDailyModel.trade_date <= end,
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StockDailyModel.adjust == adjust,
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)
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.order_by(StockDailyModel.symbol, StockDailyModel.trade_date)
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.execution_options(yield_per=20000)
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@@ -75,6 +75,7 @@ def load_daily_df(
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start: date,
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end: date,
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columns: list[str],
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adjust: str = "none",
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) -> pd.DataFrame:
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"""从 Repository 装配行情长表(供研究/选股共用)。
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@@ -86,12 +87,14 @@ def load_daily_df(
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streamer = getattr(daily_repo, "stream_range_many_columns", None)
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if streamer is not None:
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try:
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return _frame_from_stream(streamer(symbols, start, end, sorted(columns)), sorted(columns))
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return _frame_from_stream(
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streamer(symbols, start, end, sorted(columns), adjust=adjust), sorted(columns)
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)
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except Exception: # noqa: BLE001 —— 流式路径失败回退旧路径(兼容非 SQL 实现)
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pass
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get_many = getattr(daily_repo, "get_range_many", None)
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if get_many is not None:
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bars = list(get_many(symbols, start, end))
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bars = list(get_many(symbols, start, end, adjust=adjust))
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else: # 兜底:逐只查询
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bars = []
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for sym in symbols:
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@@ -134,5 +137,10 @@ class ResearchService:
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# 引擎所需列裁剪(LocalEngine 只取 close + 因子字段;Qlib 回测取全 OHLCV)
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required = self._engine.required_columns(spec)
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return load_daily_df(
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self._daily_repo, [s.symbol for s in stocks], data_start, end, sorted(required)
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self._daily_repo,
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[s.symbol for s in stocks],
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data_start,
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end,
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sorted(required),
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adjust=spec.price_adjustment,
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)
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@@ -48,7 +48,7 @@ def client(tmp_path) -> TestClient:
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bars = bars_dataframe_to_daily_bars(daily_df)
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class _MemDailyRepo:
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def get_range_many(self, symbols, start, end):
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def get_range_many(self, symbols, start, end, adjust="none"):
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out = []
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for b in bars:
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if b.symbol in symbols and start <= b.trade_date <= end:
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@@ -59,7 +59,7 @@ class _FakeDailyRepo:
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def __init__(self, bars):
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self._bars = bars
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def get_range_many(self, symbols, start, end):
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def get_range_many(self, symbols, start, end, adjust="none"):
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return [b for b in self._bars if b.symbol in symbols and start <= b.trade_date <= end]
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def get_range(self, symbol, start, end):
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@@ -0,0 +1,106 @@
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"""M7.3 行情口径测试:Repository 读路径按 adjust 过滤(不复权主口径),spec 记录口径。
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混合行场景:同 symbol/date 存在 tushare/none 与 sina/qfq 行时,研究读取
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(get_range_many / stream)默认只取 adjust=none —— 消除「混合口径污染因子」风险。
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"""
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from __future__ import annotations
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from datetime import date
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from decimal import Decimal
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from app.domain.entities.market import DailyBar
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from app.domain.entities.research import ResearchSpec
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from app.domain.entities.selection import SelectionQuery
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from app.infrastructure.persistence.sqlalchemy.base import Base
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from app.infrastructure.persistence.sqlalchemy.repositories.market_impl import (
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SqlAlchemyDailyBarRepository,
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)
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from sqlalchemy import create_engine
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from sqlalchemy.orm import Session
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_D = date(2024, 6, 3)
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_D2 = date(2024, 6, 4)
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_D3 = date(2024, 6, 5)
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def _bar(adjust: str, close: str, source: str = "tushare", day=None) -> DailyBar:
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return DailyBar(
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symbol="600519.SH",
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trade_date=day or _D,
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source=source,
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adjust=adjust,
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open=Decimal("100"), high=Decimal("101"), low=Decimal("99"),
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close=Decimal(close), volume=Decimal("1000"), amount=Decimal("100000"),
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)
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class TestAdjustFilter:
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def _session(self, tmp_path) -> Session:
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engine = create_engine(f"sqlite:///{tmp_path / 'adj.db'}", future=True)
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Base.metadata.create_all(engine)
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return Session(engine)
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def test_get_range_many_filters_adjust(self, tmp_path) -> None:
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with self._session(tmp_path) as session:
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repo = SqlAlchemyDailyBarRepository(session)
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# 唯一键 (symbol, trade_date):同键共存不可能 —— 用连续三天模拟
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# none 主口径两天 + sina/qfq 兜底一天
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repo.upsert_many(
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[
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_bar("none", "1700", day=_D),
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_bar("none", "1710", day=_D2),
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_bar("qfq", "1680", source="sina", day=_D3),
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]
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)
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session.commit()
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none_rows = repo.get_range_many(["600519.SH"], _D, _D3, adjust="none")
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assert len(none_rows) == 2 and {float(r.close) for r in none_rows} == {1700, 1710}
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qfq_rows = repo.get_range_many(["600519.SH"], _D, _D3, adjust="qfq")
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assert len(qfq_rows) == 1 and float(qfq_rows[0].close) == 1680
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def test_stream_filters_adjust(self, tmp_path) -> None:
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with self._session(tmp_path) as session:
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repo = SqlAlchemyDailyBarRepository(session)
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repo.upsert_many(
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[
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_bar("none", "1700", day=_D),
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_bar("none", "1710", day=_D2),
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_bar("qfq", "1680", source="sina", day=_D3),
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]
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)
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session.commit()
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rows = list(
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repo.stream_range_many_columns(
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["600519.SH"], _D, _D3, ["close"], adjust="none"
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)
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)
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assert len(rows) == 2 and {float(r[-1]) for r in rows} == {1700, 1710}
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qrows = list(
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repo.stream_range_many_columns(
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["600519.SH"], _D, _D3, ["close"], adjust="qfq"
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)
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)
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assert len(qrows) == 1 and float(qrows[0][-1]) == 1680
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class TestSpecRecordsAdjustment:
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def test_research_spec_default_and_field(self) -> None:
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spec = ResearchSpec(
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type="backtest",
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factors=[{"name": "momentum_60", "weight": 1}],
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period=(date(2024, 1, 1), date(2024, 6, 1)),
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)
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assert spec.price_adjustment == "none"
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snap = spec.model_dump(mode="json")
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assert snap["price_adjustment"] == "none" # 结果 config_snapshot 可溯源
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def test_selection_query_adjustment_in_snapshot(self) -> None:
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q = SelectionQuery(factors=[{"name": "momentum_60", "weight": 1}], top_n=5)
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assert q.price_adjustment == "none"
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assert q.model_dump()["price_adjustment"] == "none"
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q2 = SelectionQuery(
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factors=[{"name": "momentum_60", "weight": 1}], top_n=5, price_adjustment="qfq"
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)
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assert q2.price_adjustment == "qfq"
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@@ -69,7 +69,7 @@ class _MemDailyRepo:
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def get_range(self, symbol, start, end) -> list[DailyBar]:
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return self._bars([symbol], start, end)
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def get_range_many(self, symbols, start, end) -> list[DailyBar]:
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def get_range_many(self, symbols, start, end, adjust="none") -> list[DailyBar]:
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return self._bars(list(symbols), start, end)
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def latest_date(self, symbol: str) -> date | None:
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@@ -42,7 +42,7 @@ class _MemDailyRepo:
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def get_range(self, symbol, start, end):
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return [b for b in self._bars if b.symbol == symbol and start <= b.trade_date <= end]
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def get_range_many(self, symbols, start, end):
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def get_range_many(self, symbols, start, end, adjust="none"):
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syms = set(symbols)
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return [b for b in self._bars if b.symbol in syms and start <= b.trade_date <= end]
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@@ -38,7 +38,7 @@ class _MemDailyRepo:
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def get_range(self, symbol, start, end):
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return [b for b in self._bars_all if b.symbol == symbol and start <= b.trade_date <= end]
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def get_range_many(self, symbols, start, end):
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def get_range_many(self, symbols, start, end, adjust="none"):
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syms = set(symbols)
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return [b for b in self._bars_all if b.symbol in syms and start <= b.trade_date <= end]
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